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Surface sliding revealed by operando monitoring of high-pressure torsion by acoustic emission

Beygelzimer, Y. ; Orlov, D. LU orcid ; Baretzky, B. ; Estrin, Y. ; Vinogradov, A. and Kulagin, R. (2024) In Materials Letters 363.
Abstract

High-pressure torsion (HPT) is widely used as a key method for microstructure control through deformation processing across a broad range of materials. However, certain gaps in process control impact its efficacy. In this study, we investigate the acoustic emission (AE) signals generated during HPT by considering commercially pure molybdenum as an example. By employing the adaptive sequential k-means algorithm, we analyse the AE stream to categorise and identify its sources. By comparing the kinetics of AE signal evolution during HPT processing at pressures of 2 GPa and 5 GPa, two distinct signal types are identified: one linked to plastic deformation and the other to workpiece slippage over HPT anvil surfaces. This research... (More)

High-pressure torsion (HPT) is widely used as a key method for microstructure control through deformation processing across a broad range of materials. However, certain gaps in process control impact its efficacy. In this study, we investigate the acoustic emission (AE) signals generated during HPT by considering commercially pure molybdenum as an example. By employing the adaptive sequential k-means algorithm, we analyse the AE stream to categorise and identify its sources. By comparing the kinetics of AE signal evolution during HPT processing at pressures of 2 GPa and 5 GPa, two distinct signal types are identified: one linked to plastic deformation and the other to workpiece slippage over HPT anvil surfaces. This research demonstrates the potential of AE tools for operando monitoring of HPT stability and detection of workpiece slippage, thereby enhancing the processing efficiency.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Acoustic emission, Cluster analysis, High-pressure torsion, Severe plastic deformation, Slippage
in
Materials Letters
volume
363
article number
136303
publisher
Elsevier
external identifiers
  • scopus:85188027635
ISSN
0167-577X
DOI
10.1016/j.matlet.2024.136303
project
Topological control of microstructures for advanced material engineering
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 The Authors
id
755928d1-a957-4536-be1c-13b7cb9bd373
date added to LUP
2024-03-27 08:16:58
date last changed
2024-03-27 15:27:30
@article{755928d1-a957-4536-be1c-13b7cb9bd373,
  abstract     = {{<p>High-pressure torsion (HPT) is widely used as a key method for microstructure control through deformation processing across a broad range of materials. However, certain gaps in process control impact its efficacy. In this study, we investigate the acoustic emission (AE) signals generated during HPT by considering commercially pure molybdenum as an example. By employing the adaptive sequential k-means algorithm, we analyse the AE stream to categorise and identify its sources. By comparing the kinetics of AE signal evolution during HPT processing at pressures of 2 GPa and 5 GPa, two distinct signal types are identified: one linked to plastic deformation and the other to workpiece slippage over HPT anvil surfaces. This research demonstrates the potential of AE tools for operando monitoring of HPT stability and detection of workpiece slippage, thereby enhancing the processing efficiency.</p>}},
  author       = {{Beygelzimer, Y. and Orlov, D. and Baretzky, B. and Estrin, Y. and Vinogradov, A. and Kulagin, R.}},
  issn         = {{0167-577X}},
  keywords     = {{Acoustic emission; Cluster analysis; High-pressure torsion; Severe plastic deformation; Slippage}},
  language     = {{eng}},
  month        = {{05}},
  publisher    = {{Elsevier}},
  series       = {{Materials Letters}},
  title        = {{Surface sliding revealed by <i>operando</i> monitoring of high-pressure torsion by acoustic emission}},
  url          = {{http://dx.doi.org/10.1016/j.matlet.2024.136303}},
  doi          = {{10.1016/j.matlet.2024.136303}},
  volume       = {{363}},
  year         = {{2024}},
}